Certainly but I didn't and still don't have the resources to write solving algo on top of the business logic that goes into the algo. I would much rather user my 20+ manufacturing ERP experience to setup the problem specific to our use-case than read research papers and implement generic CS algos.

> don't have the resources to write solving algo on top of the business logic that goes into the algo

You already did that once, for your simulated annealing code.

Doesn't have to be the best, just has to be good enough.

would you be able to offer some examples and some discussion of how to choose?

> Handbook of Metaheuristics

There is a third edition, 9 years later: https://link.springer.com/book/10.1007/978-3-319-91086-4

Great coverage — simulated annealing, genetic algorithms, ant colony optimization.

Genetic algorithms are known to produce quasi-optimal results in a short time, if set up accordingly. They can be also applied to problems where constraints are very complex to explicitly formulate. Designing one is no picnic, though, and always problem-specific.

Interesting.

What do you mean by quasi optimal?

Genetic algorithms product quasi optimal results but simulated annealing will not?

Quasi-optimal means you can't prove that the algorithm will always find the optimal solution, though for all practical purposes, the algorithm will find it for over 99% of the time.

cplex comes with a great heuristic now (odh), and gurobi too.